Carbon instensity of Sustainable PV for Energy Transition¶

This analysis explores the carbon implications of different PV sustainability/circular economy designs in the context of achieving energy transition. These calculations build upon previous work that can be found in journals 13 and 17.

Attempt 1

In [1]:
import numpy as np
import pandas as pd
import os,sys
from pathlib import Path
import matplotlib.pyplot as plt

cwd = os.getcwd() #grabs current working directory

testfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'TEMP' / 'CarbonAnalysis')
inputfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'TEMP')
baselinesfolder = str(Path().resolve().parent.parent /'PV_ICE' / 'baselines')
supportMatfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'baselines' / 'SupportingMaterial')
carbonfolder = str(Path().resolve().parent.parent / 'PV_ICE'/ 'baselines'/ 'CarbonLayer')
altBaselinesfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'baselines' / 'Energy_CellModuleTechCompare')
energyanalysisfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'TEMP' / 'EnergyAnalysis')

if not os.path.exists(testfolder):
    os.makedirs(testfolder)
In [2]:
from platform import python_version 
print(python_version())

3.8.8
In [3]:
import PV_ICE
PV_ICE.__version__
Out[3]:
'v0.2.0+530.g0fcda0e.dirty'
In [4]:
#https://www.learnui.design/tools/data-color-picker.html#palette
#color pallette - modify here for all graphs below
colorpalette=['#000000', #PV ICE baseline
              '#595959', '#7F7F7F', '#A6A6A6', '#D9D9D9', #BAU, 4 grays, perc, shj, topcon, irena
              #'#067872','#0aa39e','#09d0cd','#00ffff', #realistic cases (4) teals, perc, shj, topcon, irena
              '#0579C1','#C00000','#FFC000', #extreme cases (3) long life, high eff, circular
                '#6E30A0','#00B3B5','#10C483', #ambitious modules (5) high eff+ long life, 50 yr perc, recycleSi, 
               '#97CB3F','#FF7E00' #circular perovskite+life, circular perovkiste+ high eff
                ] 

colormats = ['#00bfbf','#ff7f0e','#1f77be','#2ca02c','#d62728','#9467BD','#8C564B', 'black'] #colors for material plots       

import matplotlib as mpl #import matplotlib
from cycler import cycler #import cycler
mpl.rcParams['axes.prop_cycle'] = cycler(color=colorpalette) #reset the default color palette of mpl

from matplotlib.ticker import (MultipleLocator, AutoMinorLocator)

plt.rcParams.update({'font.size': 14})
plt.rcParams['figure.figsize'] = (8, 6)

scennames_labels = ['PV_ICE','PERC','SHJ','TOPCon','Low\nQuality',
                         'Long-Lived','High Eff','Circular',
                        'High Eff\n+ Long-life','Long-Life\n+ Recycling',
                         'Recycled-Si\n+ Long-life','Circular\n+ Long-life',
                        'Circular\n+ High Eff'
                    ]  

scennames_labels_flat = ['PV_ICE','PERC','SHJ','TOPCon','Low Quality',
                         'Long-Lived','High Eff','Circular',
                        'High Eff + Long-life','Long-Life + Recycling',
                         'Recycled-Si + Long-life','Circular + Long-life',
                        'Circular + High Eff'
                    ]
In [5]:
MATERIALS = ['glass', 'silicon', 'silver', 'aluminium_frames', 'copper', 'encapsulant', 'backsheet']
moduleFile_m = os.path.join(baselinesfolder, 'baseline_modules_mass_US.csv')
moduleFile_e = os.path.join(baselinesfolder, 'baseline_modules_energy.csv')
In [6]:
#load in the simulation from Energy Analysis journal
sim1 = PV_ICE.Simulation.load_Simpickle(filename=r'C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\TEMP\EnergyAnalysis\sim1.pkl')

sim1.calculateCarbonFlows()

sim1.scenario['r_PERC'].dataOut_c

In [7]:
sim1.scenario['r_PERC'].dataOut_m
Out[7]:
Area Cumulative_Active_Area EOL_BadStatus EOL_Landfill0 EOL_PATHS EOL_PG Effective_Capacity_[W] Landfill_0_ProjLife MerchantTail_Area MerchantTail_[W] ... Yearly_Sum_Area_PathsBad Yearly_Sum_Area_PathsGood Yearly_Sum_Area_atEOL Yearly_Sum_Power_EOLby_Degradation Yearly_Sum_Power_EOLby_Failure Yearly_Sum_Power_EOLby_ProjectLifetime Yearly_Sum_Power_PathsBad Yearly_Sum_Power_PathsGood Yearly_Sum_Power_atEOL irradiance_stc
0 5.237421e+06 5.237421e+06 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 7.520622e+08 0.000000e+00 0.0 0.0 ... 0.000000e+00 0.000000e+00 0.000000e+00 0.0 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 1070.0
1 2.381558e+06 7.618978e+06 5.019410e-03 2.844333e-02 5.019410e-03 0.000000e+00 1.092986e+09 0.000000e+00 0.0 0.0 ... 3.346274e-02 0.000000e+00 3.346274e-02 0.0 4.769010e+00 0.000000e+00 4.769010e+00 0.000000e+00 4.769010e+00 1070.0
2 2.399211e+06 1.001819e+07 3.883317e-01 2.200546e+00 3.883317e-01 0.000000e+00 1.437059e+09 0.000000e+00 0.0 0.0 ... 2.588878e+00 0.000000e+00 2.588878e+00 0.0 3.662374e+02 0.000000e+00 3.662374e+02 0.000000e+00 3.662374e+02 1070.0
3 3.636895e+06 1.365505e+07 4.784439e+00 2.711182e+01 4.784439e+00 0.000000e+00 1.965030e+09 0.000000e+00 0.0 0.0 ... 3.189626e+01 0.000000e+00 3.189626e+01 0.0 4.481373e+03 0.000000e+00 4.481373e+03 0.000000e+00 4.481373e+03 1070.0
4 7.372109e+06 2.102697e+07 2.774546e+01 1.572243e+02 2.774546e+01 0.000000e+00 3.051979e+09 0.000000e+00 0.0 0.0 ... 1.849697e+02 0.000000e+00 1.849697e+02 0.0 2.582007e+04 0.000000e+00 2.582007e+04 0.000000e+00 2.582007e+04 1070.0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
96 7.591823e+09 3.541404e+11 1.667387e+08 1.552650e+09 4.657949e+09 4.491210e+09 8.508872e+13 1.497070e+09 0.0 0.0 ... 2.223183e+08 4.491210e+09 6.210598e+09 0.0 4.896471e+10 1.297565e+12 4.896471e+10 9.731736e+11 1.346529e+12 1070.0
97 8.003568e+09 3.554404e+11 1.278019e+08 1.675878e+09 5.027633e+09 4.899831e+09 8.530804e+13 1.633277e+09 0.0 0.0 ... 1.704025e+08 4.899831e+09 6.703511e+09 0.0 3.775151e+10 1.415620e+12 3.775151e+10 1.061715e+12 1.453372e+12 1070.0
98 4.560406e+09 3.574687e+11 1.455385e+08 6.330470e+08 1.899141e+09 1.753602e+09 8.552736e+13 5.845342e+08 0.0 0.0 ... 1.940513e+08 1.753602e+09 2.532188e+09 0.0 4.301322e+10 5.066369e+11 4.301322e+10 3.799777e+11 5.496501e+11 1070.0
99 4.619609e+09 3.594903e+11 1.708910e+08 6.494993e+08 1.948498e+09 1.777607e+09 8.574668e+13 5.925356e+08 0.0 0.0 ... 2.278547e+08 1.777607e+09 2.597997e+09 0.0 5.050157e+10 5.135720e+11 5.050157e+10 3.851790e+11 5.640736e+11 1070.0
100 4.700811e+09 3.615007e+11 2.067210e+08 6.725818e+08 2.017745e+09 1.811024e+09 8.596600e+13 6.036748e+08 0.0 0.0 ... 2.756280e+08 1.811024e+09 2.690327e+09 0.0 6.103742e+10 5.232268e+11 6.103742e+10 3.924201e+11 5.842642e+11 1070.0

101 rows × 45 columns

Project grid forward to 100% re in 2050¶

To parallel the PV deployment, we will assume that we globally hit 100% RE in 2050 with the 75 TW of PV. As such, we need to change the future projection of marketshares of the different country grids.

One scenario with decarb grid, one scenario with decarb grid and heat

Estimating that 60-70% generation will be from Solar, 30-40% from wind, and any remainder from "other renewables"

In [8]:
countrygridmix = pd.read_csv(os.path.join(carbonfolder,'baseline_countrygridmix.csv'), index_col='year')
gridsources = ['Bioenergy','Hydro','Nuclear','OtherFossil','OtherRenewables','Solar','Wind']
nonRE = ['Coal','Gas','OtherFossil','Nuclear','Bioenergy']
In [9]:
countrygridmix.loc[2023:,:]=np.nan #delete 2023 to 2050
nonRE_search = '|'.join(nonRE) #create nonRE search
countrygridmix.loc[2050, countrygridmix.columns.str.contains(nonRE_search)] = 0.0 #set all nonRE to 0 in 2050
In [10]:
countrygridmix.loc[2050, countrygridmix.columns.str.contains('Solar')] = 63.0
countrygridmix.loc[2050, countrygridmix.columns.str.contains('Wind')] = 33.0
countrygridmix.loc[2050, countrygridmix.columns.str.contains('Hydro')] = 3.0
countrygridmix.loc[2050, countrygridmix.columns.str.contains('OtherRenewables')] = 1.0
#numbers derived from leading scenario electricity generation Breyer et al 2022 scenarios (EU focused)
In [11]:
countrygridmix_100RE2050 = countrygridmix.interpolate() #linearly interpolate between 2022 and 2050
In [12]:
apnd_idx = pd.RangeIndex(start=2051,stop=2101,step=1) #create temp df
apnd_df = pd.DataFrame(columns=countrygridmix_100RE2050.columns, index=apnd_idx )
countrygridmix_100RE20502100 = pd.concat([countrygridmix_100RE2050.loc[2000:],apnd_df], axis=0) #extend through 2100
countrygridmix_100RE20502100.ffill(inplace=True) #propogate 2050 values through 2100
In [13]:
countrygridmix_100RE20502100.loc[2050]
Out[13]:
China_Bioenergy            0.0
China_Coal                 0.0
China_Gas                  0.0
China_Hydro                3.0
China_Nuclear              0.0
                          ... 
Zambia_Nuclear             0.0
Zambia_OtherFossil         0.0
Zambia_OtherRenewables     1.0
Zambia_Solar              63.0
Zambia_Wind               33.0
Name: 2050, Length: 472, dtype: float64

This is a simple projection, assumes all countries have same ratio of PV and wind (which we know can't be true). Update in future with country specific projections.

In [14]:
pd.read_csv(os.path.join(carbonfolder,'baseline_electricityemissionfactors.csv'), index_col=[0])
Out[14]:
CO2eq_gpWh_IPCC2006 CO2eq_gpWh_ember CO2_gpWh_EIA CO2_gpWh_EPA
Energy Source
Bioenergy 0.3005 0.230 0.0000 0.3170
Coal 0.3487 0.820 0.3215 0.3380
Gas 0.2291 0.490 0.1805 0.1810
Hydro 0.0000 0.024 0.0000 0.0000
Nuclear 0.0000 0.012 0.0000 0.0000
OtherFossil 0.2671 0.700 0.2413 0.0000
OtherRenewables 0.0000 0.038 0.0000 0.0000
Solar 0.0000 0.048 0.0000 0.0000
Wind 0.0000 0.011 0.0000 0.0000
SteamAndHeat 0.0000 0.000 0.0000 0.2266
In [15]:
sim1.calculateCarbonFlows(countrygridmixes=countrygridmix_100RE20502100)


>>>> Calculating Carbon Flows <<<<

Working on Scenario:  PV_ICE
********************
Working on Carbon for Module
==> Working on Carbon for Material :  glass
==> Working on Carbon for Material :  silicon
==> Working on Carbon for Material :  silver
==> Working on Carbon for Material :  aluminium_frames
==> Working on Carbon for Material :  copper
==> Working on Carbon for Material :  encapsulant
==> Working on Carbon for Material :  backsheet
Working on Scenario:  r_PERC
********************
Working on Carbon for Module
==> Working on Carbon for Material :  glass
==> Working on Carbon for Material :  silicon
==> Working on Carbon for Material :  silver
==> Working on Carbon for Material :  aluminium_frames
==> Working on Carbon for Material :  copper
==> Working on Carbon for Material :  encapsulant
==> Working on Carbon for Material :  backsheet
Working on Scenario:  r_SHJ
********************
Working on Carbon for Module
==> Working on Carbon for Material :  glass
==> Working on Carbon for Material :  silicon
==> Working on Carbon for Material :  silver
==> Working on Carbon for Material :  aluminium_frames
==> Working on Carbon for Material :  copper
==> Working on Carbon for Material :  encapsulant
==> Working on Carbon for Material :  backsheet
Working on Scenario:  r_TOPCon
********************
Working on Carbon for Module
==> Working on Carbon for Material :  glass
==> Working on Carbon for Material :  silicon
==> Working on Carbon for Material :  silver
==> Working on Carbon for Material :  aluminium_frames
==> Working on Carbon for Material :  copper
==> Working on Carbon for Material :  encapsulant
==> Working on Carbon for Material :  backsheet
Working on Scenario:  r_IRENA
********************
Working on Carbon for Module
==> Working on Carbon for Material :  glass
==> Working on Carbon for Material :  silicon
==> Working on Carbon for Material :  silver
==> Working on Carbon for Material :  aluminium_frames
==> Working on Carbon for Material :  copper
==> Working on Carbon for Material :  encapsulant
==> Working on Carbon for Material :  backsheet
Working on Scenario:  ex_Life
********************
Working on Carbon for Module
==> Working on Carbon for Material :  glass
==> Working on Carbon for Material :  silicon
==> Working on Carbon for Material :  silver
==> Working on Carbon for Material :  aluminium_frames
==> Working on Carbon for Material :  copper
==> Working on Carbon for Material :  encapsulant
==> Working on Carbon for Material :  backsheet
Working on Scenario:  ex_High_eff
********************
Working on Carbon for Module
==> Working on Carbon for Material :  glass
==> Working on Carbon for Material :  silicon
==> Working on Carbon for Material :  silver
==> Working on Carbon for Material :  aluminium_frames
==> Working on Carbon for Material :  copper
==> Working on Carbon for Material :  encapsulant
==> Working on Carbon for Material :  backsheet
Working on Scenario:  ex_Circular
********************
Working on Carbon for Module
==> Working on Carbon for Material :  glass
==> Working on Carbon for Material :  silicon
==> Working on Carbon for Material :  silver
==> Working on Carbon for Material :  aluminium_frames
==> Working on Carbon for Material :  copper
==> Working on Carbon for Material :  encapsulant
==> Working on Carbon for Material :  backsheet
Working on Scenario:  h_EffLife
********************
Working on Carbon for Module
==> Working on Carbon for Material :  glass
==> Working on Carbon for Material :  silicon
==> Working on Carbon for Material :  silver
==> Working on Carbon for Material :  aluminium_frames
==> Working on Carbon for Material :  copper
==> Working on Carbon for Material :  encapsulant
==> Working on Carbon for Material :  backsheet
Working on Scenario:  h_50PERC
********************
Working on Carbon for Module
==> Working on Carbon for Material :  glass
==> Working on Carbon for Material :  silicon
==> Working on Carbon for Material :  silver
==> Working on Carbon for Material :  aluminium_frames
==> Working on Carbon for Material :  copper
==> Working on Carbon for Material :  encapsulant
==> Working on Carbon for Material :  backsheet
Working on Scenario:  h_RecycledPERC
********************
Working on Carbon for Module
==> Working on Carbon for Material :  glass
==> Working on Carbon for Material :  silicon
==> Working on Carbon for Material :  silver
==> Working on Carbon for Material :  aluminium_frames
==> Working on Carbon for Material :  copper
==> Working on Carbon for Material :  encapsulant
==> Working on Carbon for Material :  backsheet
Working on Scenario:  h_Perovskite_life
********************
Working on Carbon for Module
==> Working on Carbon for Material :  glass
==> Working on Carbon for Material :  silicon
==> Working on Carbon for Material :  silver
==> Working on Carbon for Material :  aluminium_frames
==> Working on Carbon for Material :  copper
==> Working on Carbon for Material :  encapsulant
==> Working on Carbon for Material :  backsheet
Working on Scenario:  h_Perovskite_Eff
********************
Working on Carbon for Module
==> Working on Carbon for Material :  glass
==> Working on Carbon for Material :  silicon
==> Working on Carbon for Material :  silver
==> Working on Carbon for Material :  aluminium_frames
==> Working on Carbon for Material :  copper
==> Working on Carbon for Material :  encapsulant
==> Working on Carbon for Material :  backsheet
In [ ]:

Carbon Analysis¶

this will become the aggregate carbon results function

In [16]:
scenarios = sim1.scenario
In [17]:
#simply group mod and mats carbon dfs by scenario
sim_carbon_dfs = pd.DataFrame()

for scen in scenarios:
    print(scen)
    mod_carbon_scen_results = sim1.scenario[scen].dataOut_c.add_prefix(str(scen+'_'))
    
    scenmatdc = pd.DataFrame()
    for mat in MATERIALS:
        print(mat)
        mat_carbon_scen_results = sim1.scenario[scen].material[mat].matdataOut_c.add_prefix(str(scen+'_'+mat+'_')) 
        scenmatdc = pd.concat([scenmatdc,mat_carbon_scen_results], axis=1) #group all material dc
    
    scen_carbon_results = pd.concat([mod_carbon_scen_results,scenmatdc], axis=1) #append mats to mod
    sim_carbon_dfs = pd.concat([sim_carbon_dfs, scen_carbon_results], axis=1) #append all scens "raw" data

#FIX INDEX of dfs
sim_carbon_dfs.index = pd.RangeIndex(start=2000,stop=2101,step=1)
    
#return sim_carbon_results, sim_annual_carbon

PV_ICE
glass
silicon
silver
aluminium_frames
copper
encapsulant
backsheet
r_PERC
glass
silicon
silver
aluminium_frames
copper
encapsulant
backsheet
r_SHJ
glass
silicon
silver
aluminium_frames
copper
encapsulant
backsheet
r_TOPCon
glass
silicon
silver
aluminium_frames
copper
encapsulant
backsheet
r_IRENA
glass
silicon
silver
aluminium_frames
copper
encapsulant
backsheet
ex_Life
glass
silicon
silver
aluminium_frames
copper
encapsulant
backsheet
ex_High_eff
glass
silicon
silver
aluminium_frames
copper
encapsulant
backsheet
ex_Circular
glass
silicon
silver
aluminium_frames
copper
encapsulant
backsheet
h_EffLife
glass
silicon
silver
aluminium_frames
copper
encapsulant
backsheet
h_50PERC
glass
silicon
silver
aluminium_frames
copper
encapsulant
backsheet
h_RecycledPERC
glass
silicon
silver
aluminium_frames
copper
encapsulant
backsheet
h_Perovskite_life
glass
silicon
silver
aluminium_frames
copper
encapsulant
backsheet
h_Perovskite_Eff
glass
silicon
silver
aluminium_frames
copper
encapsulant
backsheet
In [18]:
#Do math on the carbon dfs, take in the output aggregate sim df
sim_annual_carbon = pd.DataFrame()
for scen in scenarios:
    mod_mfg_carbon_total = sim_carbon_dfs.filter(like=scen).filter(like='Global_mod_MFG') #annual mfging elec carbon
    mod_nonvMFG = ['Install','OandM','Repair','Demount','Store','Resell','ReMFG','Recycle'] #could remove from loop
    nonvMFG_search = '|'.join(mod_nonvMFG) #create nonRE search
    mod_carbon_sum_nonvmfg = sim_carbon_dfs.loc[:,sim_carbon_dfs.columns.str.contains(nonvMFG_search)].filter(like=scen).filter(like='_mod_') #annual non mfging carbon
    scen_annual_carbon_mod = pd.concat([mod_mfg_carbon_total,mod_carbon_sum_nonvmfg], axis=1)
    scen_annual_carbon_mod[scen+'_Annual_Emit_mod_elec_gCO2eq'] = scen_annual_carbon_mod.sum(axis=1)

    scenmatdcmaths = pd.DataFrame()
    for mat in MATERIALS:
        scen_mat_dc_temp = sim_carbon_dfs.filter(like=scen).filter(like=mat)
        #calculation for annual carbon emissions total (selecting to avoid double countings)
        mat_global_vmfg_elec = scen_mat_dc_temp.filter(like='Global_vmfg_elec') #select global mod mfging
        mat_vmfg_countries = scen_mat_dc_temp.filter(like='vmfg_elec') #select country specific mod mfging, includes global
        mat_emit_lifecycle = scen_mat_dc_temp.loc[:,~scen_mat_dc_temp.columns.isin(mat_vmfg_countries.columns)] #select everything not the two above
        
        scen_mat_annual_carbon = pd.concat([mat_global_vmfg_elec,mat_emit_lifecycle], axis=1) #group global mod, lifecycle
        scen_mat_annual_carbon[scen+'_Annual_Emit_'+mat+'_gCO2eq'] = scen_mat_annual_carbon.sum(axis=1) #sum annual emit
        
        scenmatdcmaths = pd.concat([scenmatdcmaths,scen_mat_annual_carbon], axis=1)
        #add by material
        #add by process, fuel, elec
        
        #mat_ce_recycle = mat_carbon_scen_results.filter(like='Recycle_e_p')
        #mat_ce_remfg = mat_carbon_scen_results.filter(like='ReMFG_clean')
        #mat_landfill = mat_carbon_scen_results.filter(like='landfill_total')
        #mat_scen_annual_carbon = pd.concat([mat_vmfg_total,mat_ce_recycle,mat_ce_remfg,mat_landfill], axis=1)
    scen_modmat_annual_carbon = pd.concat([scen_annual_carbon_mod,scenmatdcmaths], axis=1)
    scen_modmat_annual_carbon[scen+'_Annual_Emit_total_modmats_gCO2eq'] = scen_modmat_annual_carbon.filter(like='Annual_Emit').sum(axis=1)
    
    sim_annual_carbon = pd.concat([sim_annual_carbon, scen_modmat_annual_carbon], axis=1)


    #FIX INDEX of dfs
sim_annual_carbon.index = pd.RangeIndex(start=2000,stop=2101,step=1)
In [19]:
#create cumulative
sim_cumu_carbon = sim_annual_carbon.cumsum()
maxy = round(sim_cumu_carbon.loc[2100].filter(like='Annual_Emit_total_modmats_gCO2eq').max()/1e12,-3) #for graphing
sim_cumu_carbon.loc[2100].filter(like='Annual_Emit_total_modmats_gCO2eq')
Out[19]:
PV_ICE_Annual_Emit_total_modmats_gCO2eq               2.964207e+16
r_PERC_Annual_Emit_total_modmats_gCO2eq               2.210524e+16
r_SHJ_Annual_Emit_total_modmats_gCO2eq                2.104457e+16
r_TOPCon_Annual_Emit_total_modmats_gCO2eq             2.148850e+16
r_IRENA_Annual_Emit_total_modmats_gCO2eq              3.221499e+16
ex_Life_Annual_Emit_total_modmats_gCO2eq              2.274670e+16
ex_High_eff_Annual_Emit_total_modmats_gCO2eq          3.053505e+16
ex_Circular_Annual_Emit_total_modmats_gCO2eq          2.804816e+16
h_EffLife_Annual_Emit_total_modmats_gCO2eq            2.295700e+16
h_50PERC_Annual_Emit_total_modmats_gCO2eq             2.283846e+16
h_RecycledPERC_Annual_Emit_total_modmats_gCO2eq       2.347228e+16
h_Perovskite_life_Annual_Emit_total_modmats_gCO2eq    2.697990e+16
h_Perovskite_Eff_Annual_Emit_total_modmats_gCO2eq     2.349308e+16
Name: 2100, dtype: float64
In [ ]:

Carbon Emissions Cumulative Scenario compare¶

In [20]:
sim_cumu_carbon_mmt = sim_cumu_carbon.filter(like='Annual_Emit_total_modmats_gCO2eq')/1e12
sim_cumu_carbon_mmt
Out[20]:
PV_ICE_Annual_Emit_total_modmats_gCO2eq r_PERC_Annual_Emit_total_modmats_gCO2eq r_SHJ_Annual_Emit_total_modmats_gCO2eq r_TOPCon_Annual_Emit_total_modmats_gCO2eq r_IRENA_Annual_Emit_total_modmats_gCO2eq ex_Life_Annual_Emit_total_modmats_gCO2eq ex_High_eff_Annual_Emit_total_modmats_gCO2eq ex_Circular_Annual_Emit_total_modmats_gCO2eq h_EffLife_Annual_Emit_total_modmats_gCO2eq h_50PERC_Annual_Emit_total_modmats_gCO2eq h_RecycledPERC_Annual_Emit_total_modmats_gCO2eq h_Perovskite_life_Annual_Emit_total_modmats_gCO2eq h_Perovskite_Eff_Annual_Emit_total_modmats_gCO2eq
2000 1.436637 1.248665 1.199394 1.223802 1.436637 1.339473 1.194566 1.436637 1.194566 1.339473 1.462665 1.436637 1.436637
2001 1.909237 1.775803 1.740827 1.758153 1.909237 1.840264 1.737400 1.909237 1.737400 1.840264 1.944515 1.909237 1.909237
2002 2.413579 2.247151 2.203526 2.225136 2.413579 2.327551 2.199252 2.413579 2.199252 2.327551 2.460202 2.413579 2.413579
2003 3.097123 2.885977 2.830631 2.858048 3.097121 2.987980 2.825208 3.097123 2.825208 2.987980 3.161400 3.097123 3.097123
2004 4.328861 4.037132 3.960664 3.998544 4.328843 4.178064 3.953171 4.328861 3.953171 4.178064 4.428316 4.328861 4.328861
... ... ... ... ... ... ... ... ... ... ... ... ... ...
2096 28440.242934 21552.285719 20518.555813 20951.276320 30544.274814 20750.854245 28271.738204 27154.070900 22397.551464 21400.300884 22542.646919 26307.102565 22731.510374
2097 28713.313526 21735.274796 20692.626559 21129.057883 30961.711458 21224.619726 28813.300118 27378.725357 22531.730102 21753.030840 22765.126159 26466.257999 22921.105303
2098 29003.781584 21857.338175 20808.742681 21247.649219 31380.505264 21716.414143 29373.501078 27602.699149 22668.934486 22111.322011 22992.241094 26626.408303 23111.228856
2099 29311.889152 21980.530888 20925.933074 21367.337730 31797.587613 22224.607777 29947.906169 27825.867869 22810.230956 22473.740092 23228.291931 26787.725830 23301.886569
2100 29642.071628 22105.244013 21044.569746 21488.503353 32214.986299 22746.698479 30535.051871 28048.159358 22957.003214 22838.456854 23472.280720 26979.902562 23493.079852

101 rows × 13 columns

In [21]:
fig_cumu_carbon, (ax1,ax2,ax3) = plt.subplots(1,3,figsize=(15,5), sharey=True, sharex=True, 
                                      gridspec_kw={'wspace':0})

#BAU
ax1.set_prop_cycle(color=colorpalette[0:5])
ax1.plot(sim_cumu_carbon_mmt.iloc[:,0:5], label=scennames_labels_flat[0:5]) # baselines
ax1.set_title('Business as Usual', fontsize=14)
ax1.set_ylabel('Cumulative Carbon Emissions\n[million metric tonnes $CO_{2eq}$]', fontsize=20)
ax1.set_xlim(2000,2100)
ax1.legend(bbox_to_anchor=(0.9,-0.05))
ax1.set_ylim(0,maxy+1000)
ax1.xaxis.set_minor_locator(MultipleLocator(10))
ax1.grid(axis='both', which='both', color='0.9', ls='--') 


#Extreme
ax2.set_prop_cycle(color=colorpalette[5:8])

ax2.plot(sim_cumu_carbon_mmt.iloc[:,5:8], label=scennames_labels_flat[5:8])
ax2.xaxis.set_minor_locator(MultipleLocator(10))
ax2.grid(axis='both', which='both', color='0.9', ls='--') 

#create glowlines for Extreme scens
n_lines = 10
diff_linewidth = 1.05
alpha_value = 0.05
for n in range(1, n_lines+1):    
    ax2.plot(sim_cumu_carbon_mmt.iloc[:,5],
            linewidth=2+(diff_linewidth*n),
            alpha=alpha_value,
            color=colorpalette[5])

for n in range(1, n_lines+1):    
    ax2.plot(sim_cumu_carbon_mmt.iloc[:,6],
            linewidth=2+(diff_linewidth*n),
            alpha=alpha_value,
            color=colorpalette[6])

for n in range(1, n_lines+1):    
    ax2.plot(sim_cumu_carbon_mmt.iloc[:,7],
            linewidth=2+(diff_linewidth*n),
            alpha=alpha_value,
            color=colorpalette[7])

ax2.set_title('Extreme', fontsize=14)
ax2.legend(bbox_to_anchor=(0.9,-0.05))

#Ambitious
ax3.set_prop_cycle(color=colorpalette[8:])
ax3.plot(sim_cumu_carbon_mmt.iloc[:,8:], label=scennames_labels_flat[8:], ls='--')# 
ax3.set_title('Ambitious', fontsize=14)
ax3.legend(bbox_to_anchor=(1.05,-0.05)) #(x,y)
ax3.xaxis.set_minor_locator(MultipleLocator(10))
ax3.grid(axis='both', which='both', color='0.9', ls='--') 

#overall figure

fig_cumu_carbon.suptitle('Cumulative Carbon Emissions', fontsize=24, y=1)
plt.show()
#fig_cumu_carbon.savefig('energyresults-annualMatDemands-decade.png', dpi=300, bbox_inches='tight')

Literature Validation¶

In [22]:
#comparing to Ember open source data, uses a lifecycle PV emission factor from IPCC for electricity carbon
ember_PVCO2 = pd.read_csv(os.path.join(carbonfolder,'Ember-PVEmissionsWorld2000-2022.csv'), index_col='year')
#ember_PVCO2['emissions_mtco2'] #ANNUAL DATA
ember_PVCO2_cumu = ember_PVCO2.cumsum()
In [23]:
#compare to Fthenakis and Leccisi 2021 analysis
FL2021_gwp_scSi2020 = 1010 #kg CO2eq/kWp from Fthenakis and Leccisi 2021 "sc_Si 2020"
FL2021_gwp_scSi2015 = 2000 #"scSi 2015"
FL2021_gwp_mcSi2020 = 1087 #mcSi 2020
FL2021_gwp_mcSi2015 = 1435 #mcSi 2015

kw_installed_pvice = sim1.scenario['PV_ICE'].dataIn_m['new_Installed_Capacity_[MW]']*1000 # kW installed

FL2021_gwp_range = pd.DataFrame(index=ember_PVCO2.index)
FL2021_gwp_range['F&L_sc-Si_2020'] = kw_installed_pvice.loc[:22].values*FL2021_gwp_scSi2020
FL2021_gwp_range['F&L_sc-Si_2015'] = kw_installed_pvice.loc[:22].values*FL2021_gwp_scSi2015
FL2021_gwp_range['F&L_mc-Si_2020'] = kw_installed_pvice.loc[:22].values*FL2021_gwp_mcSi2020
FL2021_gwp_range['F&L_mc-Si_2015'] = kw_installed_pvice.loc[:22].values*FL2021_gwp_mcSi2015

FL2021_gwp_range_cumu_mmt = FL2021_gwp_range.cumsum()/1e9 #cumulative, and kg to million metric tonnes
In [24]:
#compare to Ultra Low Carbon Solar Alliance South Korea rating, as redproduced in Polverini 2023
Polverini2023_low = 550 #kg CO2eq/kWp "France"
Polverini2023_high = 762 #kg CO2eq/kWp "China"

kw_installed_pvice = sim1.scenario['PV_ICE'].dataIn_m['new_Installed_Capacity_[MW]']*1000 # kW installed

Polverini2023_gwp_range = pd.DataFrame(index=ember_PVCO2.index)
Polverini2023_gwp_range['Polverini2023_low'] = kw_installed_pvice.loc[:22].values*Polverini2023_low
Polverini2023_gwp_range['Polverini2023_high'] = kw_installed_pvice.loc[:22].values*Polverini2023_high

Polverini2023_gwp_range_cumu_mmt = Polverini2023_gwp_range.cumsum()/1e9 #cumulative, and kg to million metric tonnes
In [25]:
#compare to Liang and You 2023, using Figure 1 2020 values from a and e
LiangYou2023_scSi_low = 250 #kg CO2eq/m2 ""
LiangYou2023_scSi_high = 350 #kg CO2eq/m2 ""
LiangYou2023_mcSi_low = 210
LiangYou2023_mcSi_high = 300

m2_installed_pvice = sim1.scenario['PV_ICE'].dataOut_m['Area'] # area deployed in m2

LiangYou2023_gwp_range = pd.DataFrame(index=ember_PVCO2.index)
LiangYou2023_gwp_range['LiangYou2023_scSi_low'] = m2_installed_pvice.loc[:22].values*LiangYou2023_scSi_low
LiangYou2023_gwp_range['LiangYou2023_scSi_high'] = m2_installed_pvice.loc[:22].values*LiangYou2023_scSi_high
LiangYou2023_gwp_range['LiangYou2023_mcSi_low'] = m2_installed_pvice.loc[:22].values*LiangYou2023_mcSi_low
LiangYou2023_gwp_range['LiangYou2023_mcSi_high'] = m2_installed_pvice.loc[:22].values*LiangYou2023_mcSi_high

LiangYou2023_gwp_range_cumu_mmt = LiangYou2023_gwp_range.cumsum()/1e9 #cumulative, and kg to million metric tonnes
In [26]:
plt.plot(sim_cumu_carbon_mmt.loc[:2022,'PV_ICE_Annual_Emit_total_modmats_gCO2eq'], label='PV_ICE', color='black')

plt.plot(LiangYou2023_gwp_range_cumu_mmt['LiangYou2023_scSi_low'], label='LiangYou2023_scSi_low', color='violet', ls='-.')
plt.plot(LiangYou2023_gwp_range_cumu_mmt['LiangYou2023_scSi_high'], label='LiangYou2023_scSi_high', color='mediumorchid', ls='-.')
plt.plot(LiangYou2023_gwp_range_cumu_mmt['LiangYou2023_mcSi_low'], label='LiangYou2023_mcSi_low', color='darkviolet', ls=':')
plt.plot(LiangYou2023_gwp_range_cumu_mmt['LiangYou2023_mcSi_high'], label='LiangYou2023_mcSi_high', color='blueviolet', ls=':')

plt.plot(FL2021_gwp_range_cumu_mmt['F&L_sc-Si_2020'], label='FthenakisLeccisi2021_sc-Si_2020', color='lightcoral', ls='-.')
plt.plot(FL2021_gwp_range_cumu_mmt.loc[:2015,'F&L_sc-Si_2015'], label='FthenakisLeccisi2021_sc-Si_2015', color='indianred', ls='-.')
plt.plot(FL2021_gwp_range_cumu_mmt['F&L_mc-Si_2020'], label='FthenakisLeccisi2021_mc-Si_2020', color='firebrick', ls='dotted')
plt.plot(FL2021_gwp_range_cumu_mmt.loc[:2015,'F&L_mc-Si_2015'], label='FthenakisLeccisi2021_mc-Si_2015', color='maroon', ls='dotted')

plt.plot(Polverini2023_gwp_range_cumu_mmt['Polverini2023_low'], label='Polverini2023_low', color='deepskyblue', ls='--')
plt.plot(Polverini2023_gwp_range_cumu_mmt['Polverini2023_high'], label='Polverini2023_high', color='dodgerblue', ls='--')

plt.plot(ember_PVCO2_cumu.index, ember_PVCO2_cumu['emissions_mtco2'], label='Ember_electricity', color='green', ls='--')

plt.ylabel('Cumulative Carbon\n[$CO_{2eq}$ million metric tonnes]')
plt.title('Cumulative Carbon Emissions from PV')
plt.xlim(2000,2025)
plt.ylim(0,)
plt.legend(loc='upper left', fontsize=12)
Out[26]:
<matplotlib.legend.Legend at 0x1fb298b8520>

Flip it, compare on CO2eq/kWp¶

This is a simple way of doing it, will not work for much beyond 2022, because the annual emissions include end of life of other systems, not just the mfging of installed - its not necessarily fair. Might work out on balance, kinda

In [94]:
#lit factors into scatter points
litfactors = pd.DataFrame(index=ember_PVCO2_cumu.index)

litfactors.loc[2022,'Polverini2023_low'] = Polverini2023_low
litfactors.loc[2022,'Polverini2023_high'] = Polverini2023_high
litfactors.loc[2020,'FthenakisLeccisi2021_scSi'] = FL2021_gwp_scSi2020 
litfactors.loc[2015,'FthenakisLeccisi2021_scSi'] = FL2021_gwp_scSi2015 
litfactors.loc[2020,'FthenakisLeccisi2021_mcSi'] = FL2021_gwp_mcSi2020
litfactors.loc[2015,'FthenakisLeccisi2021_mcSi'] = FL2021_gwp_mcSi2015

litfactors.loc[2020,'LiangYou2023_scSi_m$^{2}$'] = LiangYou2023_scSi_low #kg CO2eq/m2 ""
litfactors.loc[2019,'LiangYou2023_scSi_m$^{2}$'] = LiangYou2023_scSi_high  #kg CO2eq/m2 ""
litfactors.loc[2020,'LiangYou2023_mcSi_m$^{2}$'] = LiangYou2023_mcSi_low 
litfactors.loc[2019,'LiangYou2023_mcSi_m$^{2}$'] = LiangYou2023_mcSi_high

litfactors.loc[2015,'Anctil2021_low'] = 1010
litfactors.loc[2015,'Anctil2021_high'] = 1775
litfactors.loc[2020,'Anctil2021_low'] = 500
litfactors.loc[2020,'Anctil2021_high'] = 750

litfactors.loc[2005,'AlsemadeWild2005_scSi'] = 41 #g/kWh/1000 = kg/kWh *1275 kw

#litfactors
In [73]:
#calculate CO2eq/kWp by dividing annual CO2eq/deployed PV
installs = pd.DataFrame(kw_installed_pvice.loc[:22])
installs.index = ember_PVCO2_cumu.index

pvice_emit_annual = sim_annual_carbon.filter(like='PV_ICE').filter(like='Annual_Emit_total_modmats_gCO2eq').loc[:2022]
pvice_annual_kgco2pkwp = pvice_emit_annual['PV_ICE_Annual_Emit_total_modmats_gCO2eq'].div(installs['new_Installed_Capacity_[MW]'], axis=0)/1e3
#pvice_annual_kgco2pkwp
In [74]:
#calculate CO2eq/m2 for PV ICE
meters2installs = pd.DataFrame(m2_installed_pvice.loc[:22])
meters2installs.index = ember_PVCO2_cumu.index

pvice_annual_kgco2eqpm2 = pvice_emit_annual['PV_ICE_Annual_Emit_total_modmats_gCO2eq'].div(meters2installs['Area'], axis=0)/1e3
#pvice_annual_kgco2eqpm2
In [92]:
litfactors.columns
Out[92]:
Index(['Polverini2023_low', 'Polverini2023_high', 'FthenakisLeccisi2021_scSi',
       'FthenakisLeccisi2021_mcSi', 'LiangYou2023_scSi_m$^{2}$',
       'LiangYou2023_mcSi_m$^{2}$'],
      dtype='object')
In [96]:
#graphing
plt.plot(pvice_annual_kgco2eqpm2, label='PV_ICE kg CO$_{2}$eq/m$^{2}$', color='darkgray')
plt.plot(pvice_annual_kgco2pkwp, label='PV_ICE kg CO$_{2}$eq/kW$_{p}$', color='black')

plt.scatter(litfactors.index, litfactors['Polverini2023_low'], label='Polverini2023_low', color='deepskyblue')
plt.scatter(litfactors.index, litfactors['Polverini2023_high'], label='Polverini2023_high', color='dodgerblue')

plt.scatter(litfactors.index, litfactors['FthenakisLeccisi2021_scSi'], label='FthenakisLeccisi2021_scSi', color='lightcoral')
plt.scatter(litfactors.index, litfactors['FthenakisLeccisi2021_mcSi'], label='FthenakisLeccisi2021_mcSi', color='firebrick')

plt.scatter(litfactors.index, litfactors['LiangYou2023_scSi_m$^{2}$'], label='LiangYou2023_scSi_m$^{2}$', color='violet')
plt.scatter(litfactors.index, litfactors['LiangYou2023_mcSi_m$^{2}$'], label='LiangYou2023_mcSi_m$^{2}$', color='darkviolet')

plt.scatter(litfactors.index, litfactors['Anctil2021_low'], label='Anctil2021_low', color='green')
plt.scatter(litfactors.index, litfactors['Anctil2021_high'], label='Anctil2021_high', color='limegreen')

plt.ylabel('kg CO$_{2}$eq/kW$_{p}$ OR kg CO$_{2}$eq/m$^{2}$')
plt.title('Literature Comparison:\nkg CO$_{2}$eq/kW$_{p}$')
plt.legend(bbox_to_anchor=(1.6,1))
Out[96]:
<matplotlib.legend.Legend at 0x1fb348effd0>

Contextualize versus Global Carbon Emissions and Budget¶

In [ ]:
In [ ]:
In [ ]:

Cabon Emissions by material or module¶

In [28]:
for scen in scenarios:

    scen_annual_carbon = sim_annual_carbon.filter(like='Annual_Emit').filter(like=scen)/1e12 #million tonnes
    
    plt.plot([],[],color=colormats[0], label=MATERIALS[0])
    plt.plot([],[],color=colormats[1], label=MATERIALS[1])
    plt.plot([],[],color=colormats[2], label=MATERIALS[2])
    plt.plot([],[],color=colormats[3], label=MATERIALS[3])
    plt.plot([],[],color=colormats[4], label=MATERIALS[4])
    plt.plot([],[],color=colormats[5], label=MATERIALS[5])
    plt.plot([],[],color=colormats[6], label=MATERIALS[6])
    plt.plot([],[],color=colormats[7], label='module')


    plt.stackplot(scen_annual_carbon.index,
                  scen_annual_carbon[scen+'_Annual_Emit_glass_gCO2eq'], 
                  scen_annual_carbon[scen+'_Annual_Emit_silicon_gCO2eq'],
                  scen_annual_carbon[scen+'_Annual_Emit_silver_gCO2eq'], 
                  scen_annual_carbon[scen+'_Annual_Emit_aluminium_frames_gCO2eq'], 
                  scen_annual_carbon[scen+'_Annual_Emit_copper_gCO2eq'],
                  scen_annual_carbon[scen+'_Annual_Emit_encapsulant_gCO2eq'],
                  scen_annual_carbon[scen+'_Annual_Emit_backsheet_gCO2eq'],
                  scen_annual_carbon[scen+'_Annual_Emit_mod_elec_gCO2eq'],
                  colors = colormats)
    plt.title(scen+':\nGHG Emissions Annually by Module and Material Lifecycle')
    plt.ylabel('GHG Emissions Annually from Lifecycle Mats and Mods\n[million metric tonnes $CO_{2eq}$]')
    plt.xlim(2000,2100)

    handles, labels = plt.gca().get_legend_handles_labels()
#specify order of items in legend
#order = [1,2,0]
#add legend to plot
#plt.legend([handles[idx] for idx in order],[labels[idx] for idx in order])
    plt.legend(handles[::-1], labels[::-1], bbox_to_anchor=(1.45,1))

#plt.legend()
    plt.show()
In [ ]:
In [29]:
maxy
Out[29]:
32000.0
In [30]:
#colormats = ['#00bfbf','#ff7f0e','#1f77be','#2ca02c','#d62728','#9467BD','#8C564B','black'] #colors for material plots
for scen in scenarios:

    scen_cumu_carbon = sim_cumu_carbon.filter(like='Annual_Emit').filter(like=scen)/1e12 #million tonnes
    
    plt.plot([],[],color=colormats[0], label=MATERIALS[0])
    plt.plot([],[],color=colormats[1], label=MATERIALS[1])
    plt.plot([],[],color=colormats[2], label=MATERIALS[2])
    plt.plot([],[],color=colormats[3], label=MATERIALS[3])
    plt.plot([],[],color=colormats[4], label=MATERIALS[4])
    plt.plot([],[],color=colormats[5], label=MATERIALS[5])
    plt.plot([],[],color=colormats[6], label=MATERIALS[6])
    plt.plot([],[],color=colormats[7], label='module')


    plt.stackplot(scen_cumu_carbon.index,
                  scen_cumu_carbon[scen+'_Annual_Emit_glass_gCO2eq'], 
                  scen_cumu_carbon[scen+'_Annual_Emit_silicon_gCO2eq'],
                  scen_cumu_carbon[scen+'_Annual_Emit_silver_gCO2eq'], 
                  scen_cumu_carbon[scen+'_Annual_Emit_aluminium_frames_gCO2eq'], 
                  scen_cumu_carbon[scen+'_Annual_Emit_copper_gCO2eq'],
                  scen_cumu_carbon[scen+'_Annual_Emit_encapsulant_gCO2eq'],
                  scen_cumu_carbon[scen+'_Annual_Emit_backsheet_gCO2eq'],
                  scen_cumu_carbon[scen+'_Annual_Emit_mod_elec_gCO2eq'],
                  colors = colormats)
    plt.title(scen+':\nGHG Emissions Annually by Module and Material Lifecycle')
    plt.ylabel('GHG Emissions Annually from Lifecycle Mats and Mods\n[million metric tonnes $CO_{2eq}$]')
    plt.xlim(2000,2100)
    plt.ylim(0,maxy+1000)

    handles, labels = plt.gca().get_legend_handles_labels()
#specify order of items in legend
#order = [1,2,0]
#add legend to plot
#plt.legend([handles[idx] for idx in order],[labels[idx] for idx in order])
    plt.legend(handles[::-1], labels[::-1], bbox_to_anchor=(1.45,1))
    plt.grid(axis='both', which='both', color='0.9', ls='--')
#plt.legend()
    plt.show()
In [47]:
sim_cumu_carbon.loc[2100].filter(like='Annual_Emit_mod_elec')
Out[47]:
PV_ICE_Annual_Emit_mod_elec_gCO2eq               8.661511e+13
r_PERC_Annual_Emit_mod_elec_gCO2eq               7.955485e+13
r_SHJ_Annual_Emit_mod_elec_gCO2eq                7.575890e+13
r_TOPCon_Annual_Emit_mod_elec_gCO2eq             7.733545e+13
r_IRENA_Annual_Emit_mod_elec_gCO2eq              1.014963e+14
ex_Life_Annual_Emit_mod_elec_gCO2eq              8.112453e+13
ex_High_eff_Annual_Emit_mod_elec_gCO2eq          6.481039e+13
ex_Circular_Annual_Emit_mod_elec_gCO2eq          1.238429e+14
h_EffLife_Annual_Emit_mod_elec_gCO2eq            6.864642e+13
h_50PERC_Annual_Emit_mod_elec_gCO2eq             8.163300e+13
h_RecycledPERC_Annual_Emit_mod_elec_gCO2eq       3.024845e+14
h_Perovskite_life_Annual_Emit_mod_elec_gCO2eq    1.173374e+14
h_Perovskite_Eff_Annual_Emit_mod_elec_gCO2eq     1.055897e+14
Name: 2100, dtype: float64
In [48]:
#create a df from which to do a bar chart of 2100 emissions by mat/mod
mats_emit_2100 = pd.DataFrame() #index=scennames_labels_flat
for mat in MATERIALS:
    mat_emit_2100 = pd.Series(sim_cumu_carbon.loc[2100].filter(like='Annual_Emit_'+mat).values)
    mats_emit_2100 = pd.concat([mats_emit_2100, mat_emit_2100], axis=1)

mats_emit_2100
mats_emit_2100.columns = MATERIALS
modmats_emit_2100 = pd.concat([mats_emit_2100,pd.Series(sim_cumu_carbon.loc[2100].filter(like='Annual_Emit_mod_elec').values)], axis=1)
modmats_emit_2100.index = scennames_labels_flat
modmats_emit_2100.rename(columns={0:'module'}, inplace=True)
modmats_emit_2100_megatonne = modmats_emit_2100/1e12
modmats_emit_2100_megatonne
Out[48]:
glass silicon silver aluminium_frames copper encapsulant backsheet module
PV_ICE 10128.661994 6393.528822 39.555545 7759.907146 8.553999 3013.443849 2211.805161 86.615111
PERC 9100.755660 5083.297261 25.191311 4986.668409 7.933845 2800.113453 21.729228 79.554845
SHJ 8652.812210 4837.013804 44.794737 4742.685932 7.545504 2662.628153 21.330504 75.758903
TOPCon 8837.278528 4938.802814 43.333633 4843.242140 7.705695 2719.277076 21.528020 77.335447
Low Quality 11526.374030 5202.740656 40.635431 7133.665114 12.970040 4731.060652 3466.044108 101.496270
Long-Lived 9270.300576 5078.154692 29.471217 6013.562121 6.784228 2244.838020 22.463092 81.124534
High Eff 12963.265329 6145.955317 38.732726 8176.784091 8.755677 3115.456836 21.291503 64.810392
Circular 8996.350286 4412.185490 92.251404 6254.983351 21.597082 8123.698405 23.250434 123.842906
High Eff + Long-life 9507.952013 4926.966042 31.290604 6098.983567 6.741955 2295.131175 21.291435 68.646424
Long-Life + Recycling 9444.863234 5171.277979 32.719877 5717.789805 6.882528 2360.827092 22.463337 81.633002
Recycled-Si + Long-life 8876.675002 2259.159910 33.593199 4978.766570 11.277104 4044.829561 2965.494878 302.484496
Circular + Long-life 8839.142638 6474.457856 66.818549 5916.944139 15.130176 5526.821345 23.250434 117.337425
Circular + High Eff 6880.106946 5865.683841 68.893415 4657.933648 15.835931 5875.785931 23.250434 105.589706
In [49]:
fig_cumuemit_modmat, (ax0,ax2,ax3) = plt.subplots(1,3,figsize=(15,8), sharey=True, 
                                      gridspec_kw={'wspace': 0, 'width_ratios': [1.5,1,1.5]})
#BAU
ax0.bar(scennames_labels[0:5], modmats_emit_2100_megatonne[0:5]['glass'], color=colormats[0])
ax0.bar(scennames_labels[0:5], modmats_emit_2100_megatonne[0:5]['silicon'],
        bottom=modmats_emit_2100_megatonne[0:5]['glass'], color=colormats[1])
ax0.bar(scennames_labels[0:5], modmats_emit_2100_megatonne[0:5]['silver'],
       bottom=modmats_emit_2100_megatonne.iloc[0:5,0:2].sum(axis=1), color=colormats[2])
ax0.bar(scennames_labels[0:5], modmats_emit_2100_megatonne[0:5]['aluminium_frames'],
       bottom=modmats_emit_2100_megatonne.iloc[0:5,0:3].sum(axis=1), color=colormats[3])
ax0.bar(scennames_labels[0:5], modmats_emit_2100_megatonne[0:5]['copper'],
       bottom=modmats_emit_2100_megatonne.iloc[0:5,0:4].sum(axis=1), color=colormats[4])
ax0.bar(scennames_labels[0:5], modmats_emit_2100_megatonne[0:5]['encapsulant'],
       bottom=modmats_emit_2100_megatonne.iloc[0:5,0:5].sum(axis=1), color=colormats[5])
ax0.bar(scennames_labels[0:5], modmats_emit_2100_megatonne[0:5]['backsheet'],
       bottom=modmats_emit_2100_megatonne.iloc[0:5,0:6].sum(axis=1), color=colormats[6])
ax0.bar(scennames_labels[0:5], modmats_emit_2100_megatonne[0:5]['module'],
       bottom=modmats_emit_2100_megatonne.iloc[0:5,0:7].sum(axis=1), color='black')

ax0.set_ylim(0,maxy+1000)
ax0.set_ylabel('Cumulative Carbon Emissions\n[million metric tonnes $CO_{2eq}$]', fontsize=20)
ax0.set_title('Baseline', fontsize=14)
ax0.set_xticklabels(labels=scennames_labels[0:5], rotation=45)
ax0.grid(axis='y', color='0.6', ls='--') 
ax0.set_axisbelow(True)

#Extreme
ax2.bar(scennames_labels[5:8], modmats_emit_2100_megatonne[5:8]['glass'], color=colormats[0])
ax2.bar(scennames_labels[5:8], modmats_emit_2100_megatonne[5:8]['silicon'],
        bottom=modmats_emit_2100_megatonne[5:8]['glass'], color=colormats[1])
ax2.bar(scennames_labels[5:8], modmats_emit_2100_megatonne[5:8]['silver'],
       bottom=modmats_emit_2100_megatonne.iloc[5:8,0:2].sum(axis=1), color=colormats[2])
ax2.bar(scennames_labels[5:8], modmats_emit_2100_megatonne[5:8]['aluminium_frames'],
       bottom=modmats_emit_2100_megatonne.iloc[5:8,0:3].sum(axis=1), color=colormats[3])
ax2.bar(scennames_labels[5:8], modmats_emit_2100_megatonne[5:8]['copper'],
       bottom=modmats_emit_2100_megatonne.iloc[5:8,0:4].sum(axis=1), color=colormats[4])
ax2.bar(scennames_labels[5:8], modmats_emit_2100_megatonne[5:8]['encapsulant'],
       bottom=modmats_emit_2100_megatonne.iloc[5:8,0:5].sum(axis=1), color=colormats[5])
ax2.bar(scennames_labels[5:8], modmats_emit_2100_megatonne[5:8]['backsheet'],
       bottom=modmats_emit_2100_megatonne.iloc[5:8,0:6].sum(axis=1), color=colormats[6])
ax2.bar(scennames_labels[5:8], modmats_emit_2100_megatonne[5:8]['module'],
       bottom=modmats_emit_2100_megatonne.iloc[5:8,0:7].sum(axis=1), color='black')

ax2.set_title('Extreme', fontsize=14)
ax2.set_xticklabels(labels=scennames_labels[5:8], rotation=45)
ax2.grid(axis='y', color='0.6', ls='--') 
ax2.set_axisbelow(True)

#Ambitious
ax3.bar(scennames_labels[8:], modmats_emit_2100_megatonne[8:]['glass'], color=colormats[0])
ax3.bar(scennames_labels[8:], modmats_emit_2100_megatonne[8:]['silicon'],
        bottom=modmats_emit_2100_megatonne[8:]['glass'], color=colormats[1])
ax3.bar(scennames_labels[8:], modmats_emit_2100_megatonne[8:]['silver'],
       bottom=modmats_emit_2100_megatonne.iloc[8:,0:2].sum(axis=1), color=colormats[2])
ax3.bar(scennames_labels[8:], modmats_emit_2100_megatonne[8:]['aluminium_frames'],
       bottom=modmats_emit_2100_megatonne.iloc[8:,0:3].sum(axis=1), color=colormats[3])
ax3.bar(scennames_labels[8:], modmats_emit_2100_megatonne[8:]['copper'],
       bottom=modmats_emit_2100_megatonne.iloc[8:,0:4].sum(axis=1), color=colormats[4])
ax3.bar(scennames_labels[8:], modmats_emit_2100_megatonne[8:]['encapsulant'],
       bottom=modmats_emit_2100_megatonne.iloc[8:,0:5].sum(axis=1), color=colormats[5])
ax3.bar(scennames_labels[8:], modmats_emit_2100_megatonne[8:]['backsheet'],
       bottom=modmats_emit_2100_megatonne.iloc[8:,0:6].sum(axis=1), color=colormats[6])
ax3.bar(scennames_labels[8:], modmats_emit_2100_megatonne[8:]['module'],
       bottom=modmats_emit_2100_megatonne.iloc[8:,0:7].sum(axis=1), color='black')


ax3.set_title('Ambitious', fontsize=14)
ax3.set_xticklabels(labels=scennames_labels[8:], rotation=45)
ax3.grid(axis='y', color='0.6', ls='--') 
ax3.set_axisbelow(True)

#overall fig

fig_cumuemit_modmat.suptitle('Cumulative Emisisons in 2100 by material', fontsize=24)
plt.show()

#fig_cumuemit_modmat.savefig('energyresults-energyBalance.png', dpi=300, bbox_inches='tight')

C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_2108\3630820325.py:23: UserWarning: FixedFormatter should only be used together with FixedLocator
  ax0.set_xticklabels(labels=scennames_labels[0:5], rotation=45)
C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_2108\3630820325.py:45: UserWarning: FixedFormatter should only be used together with FixedLocator
  ax2.set_xticklabels(labels=scennames_labels[5:8], rotation=45)
C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_2108\3630820325.py:68: UserWarning: FixedFormatter should only be used together with FixedLocator
  ax3.set_xticklabels(labels=scennames_labels[8:], rotation=45)
In [50]:
cumu_emit_sum = modmats_emit_2100_megatonne.sum(axis=1)
fraction_modmats_cumu_emit = modmats_emit_2100_megatonne.div(cumu_emit_sum, axis=0)*100
fraction_modmats_cumu_emit
Out[50]:
glass silicon silver aluminium_frames copper encapsulant backsheet module
PV_ICE 34.169886 21.569103 0.133444 26.178694 0.028858 10.166104 7.461709 0.292203
PERC 41.170121 22.995888 0.113961 22.558758 0.035891 12.667191 0.098299 0.359891
SHJ 41.116603 22.984617 0.212857 22.536388 0.035855 12.652329 0.101359 0.359993
TOPCon 41.125612 22.983466 0.201660 22.538760 0.035860 12.654567 0.100184 0.359892
Low Quality 35.779540 16.150063 0.126138 22.143933 0.040261 14.685900 10.759105 0.315059
Long-Lived 40.754488 22.324799 0.129563 26.437077 0.029825 9.868852 0.098753 0.356643
High Eff 42.453720 20.127542 0.126847 26.778353 0.028674 10.202887 0.069728 0.212249
Circular 32.074655 15.730749 0.328904 22.300869 0.077000 28.963392 0.082895 0.441537
High Eff + Long-life 41.416347 21.461713 0.136301 26.566985 0.029368 9.997521 0.092745 0.299022
Long-Life + Recycling 41.355085 22.642852 0.143267 25.035797 0.030136 10.337069 0.098358 0.357437
Recycled-Si + Long-life 37.817693 9.624799 0.143119 21.211260 0.048044 17.232367 12.634030 1.288688
Circular + Long-life 32.761952 23.997336 0.247660 21.930932 0.056079 20.484957 0.086177 0.434907
Circular + High Eff 29.285675 24.967709 0.293250 19.826833 0.067407 25.010709 0.098967 0.449450
In [51]:
fig_cumuemit_modmat, (ax0,ax2,ax3) = plt.subplots(1,3,figsize=(15,8), sharey=True, 
                                      gridspec_kw={'wspace': 0, 'width_ratios': [1.5,1,1.5]})
#BAU
ax0.bar(scennames_labels[0:5], fraction_modmats_cumu_emit[0:5]['glass'], color=colormats[0])
ax0.bar(scennames_labels[0:5], fraction_modmats_cumu_emit[0:5]['silicon'],
        bottom=fraction_modmats_cumu_emit[0:5]['glass'], color=colormats[1])
ax0.bar(scennames_labels[0:5], fraction_modmats_cumu_emit[0:5]['silver'],
       bottom=fraction_modmats_cumu_emit.iloc[0:5,0:2].sum(axis=1), color=colormats[2])
ax0.bar(scennames_labels[0:5], fraction_modmats_cumu_emit[0:5]['aluminium_frames'],
       bottom=fraction_modmats_cumu_emit.iloc[0:5,0:3].sum(axis=1), color=colormats[3])
ax0.bar(scennames_labels[0:5], fraction_modmats_cumu_emit[0:5]['copper'],
       bottom=fraction_modmats_cumu_emit.iloc[0:5,0:4].sum(axis=1), color=colormats[4])
ax0.bar(scennames_labels[0:5], fraction_modmats_cumu_emit[0:5]['encapsulant'],
       bottom=fraction_modmats_cumu_emit.iloc[0:5,0:5].sum(axis=1), color=colormats[5])
ax0.bar(scennames_labels[0:5], fraction_modmats_cumu_emit[0:5]['backsheet'],
       bottom=fraction_modmats_cumu_emit.iloc[0:5,0:6].sum(axis=1), color=colormats[6])
ax0.bar(scennames_labels[0:5], fraction_modmats_cumu_emit[0:5]['module'],
       bottom=fraction_modmats_cumu_emit.iloc[0:5,0:7].sum(axis=1), color='black')

ax0.set_ylim(0,100)
ax0.set_ylabel('Percent Emissions by Material\n[%]', fontsize=20)
ax0.set_title('Baseline', fontsize=14)
ax0.set_xticklabels(labels=scennames_labels[0:5], rotation=45)
ax0.grid(axis='y', color='0.6', ls='--') 
ax0.set_axisbelow(True)

#Extreme
ax2.bar(scennames_labels[5:8], fraction_modmats_cumu_emit[5:8]['glass'], color=colormats[0])
ax2.bar(scennames_labels[5:8], fraction_modmats_cumu_emit[5:8]['silicon'],
        bottom=fraction_modmats_cumu_emit[5:8]['glass'], color=colormats[1])
ax2.bar(scennames_labels[5:8], fraction_modmats_cumu_emit[5:8]['silver'],
       bottom=fraction_modmats_cumu_emit.iloc[5:8,0:2].sum(axis=1), color=colormats[2])
ax2.bar(scennames_labels[5:8], fraction_modmats_cumu_emit[5:8]['aluminium_frames'],
       bottom=fraction_modmats_cumu_emit.iloc[5:8,0:3].sum(axis=1), color=colormats[3])
ax2.bar(scennames_labels[5:8], fraction_modmats_cumu_emit[5:8]['copper'],
       bottom=fraction_modmats_cumu_emit.iloc[5:8,0:4].sum(axis=1), color=colormats[4])
ax2.bar(scennames_labels[5:8], fraction_modmats_cumu_emit[5:8]['encapsulant'],
       bottom=fraction_modmats_cumu_emit.iloc[5:8,0:5].sum(axis=1), color=colormats[5])
ax2.bar(scennames_labels[5:8], fraction_modmats_cumu_emit[5:8]['backsheet'],
       bottom=fraction_modmats_cumu_emit.iloc[5:8,0:6].sum(axis=1), color=colormats[6])
ax2.bar(scennames_labels[5:8], fraction_modmats_cumu_emit[5:8]['module'],
       bottom=fraction_modmats_cumu_emit.iloc[5:8,0:7].sum(axis=1), color='black')

ax2.set_title('Extreme', fontsize=14)
ax2.set_xticklabels(labels=scennames_labels[5:8], rotation=45)
ax2.grid(axis='y', color='0.6', ls='--') 
ax2.set_axisbelow(True)

#Ambitious
ax3.bar(scennames_labels[8:], fraction_modmats_cumu_emit[8:]['glass'], color=colormats[0])
ax3.bar(scennames_labels[8:], fraction_modmats_cumu_emit[8:]['silicon'],
        bottom=fraction_modmats_cumu_emit[8:]['glass'], color=colormats[1])
ax3.bar(scennames_labels[8:], fraction_modmats_cumu_emit[8:]['silver'],
       bottom=fraction_modmats_cumu_emit.iloc[8:,0:2].sum(axis=1), color=colormats[2])
ax3.bar(scennames_labels[8:], fraction_modmats_cumu_emit[8:]['aluminium_frames'],
       bottom=fraction_modmats_cumu_emit.iloc[8:,0:3].sum(axis=1), color=colormats[3])
ax3.bar(scennames_labels[8:], fraction_modmats_cumu_emit[8:]['copper'],
       bottom=fraction_modmats_cumu_emit.iloc[8:,0:4].sum(axis=1), color=colormats[4])
ax3.bar(scennames_labels[8:], fraction_modmats_cumu_emit[8:]['encapsulant'],
       bottom=fraction_modmats_cumu_emit.iloc[8:,0:5].sum(axis=1), color=colormats[5])
ax3.bar(scennames_labels[8:], fraction_modmats_cumu_emit[8:]['backsheet'],
       bottom=fraction_modmats_cumu_emit.iloc[8:,0:6].sum(axis=1), color=colormats[6])
ax3.bar(scennames_labels[8:], fraction_modmats_cumu_emit[8:]['module'],
       bottom=fraction_modmats_cumu_emit.iloc[8:,0:7].sum(axis=1), color='black')


ax3.set_title('Ambitious', fontsize=14)
ax3.set_xticklabels(labels=scennames_labels[8:], rotation=45)
ax3.grid(axis='y', color='0.6', ls='--') 
ax3.set_axisbelow(True)

#overall fig

fig_cumuemit_modmat.suptitle('Fraction of Emissions attribute to Module/Material', fontsize=24)
plt.show()

#fig_cumuemit_modmat.savefig('energyresults-energyBalance.png', dpi=300, bbox_inches='tight')

C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_2108\2389923115.py:23: UserWarning: FixedFormatter should only be used together with FixedLocator
  ax0.set_xticklabels(labels=scennames_labels[0:5], rotation=45)
C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_2108\2389923115.py:45: UserWarning: FixedFormatter should only be used together with FixedLocator
  ax2.set_xticklabels(labels=scennames_labels[5:8], rotation=45)
C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_2108\2389923115.py:68: UserWarning: FixedFormatter should only be used together with FixedLocator
  ax3.set_xticklabels(labels=scennames_labels[8:], rotation=45)
In [ ]:

Cumulative Carbon in 2050 and 2100¶

In [52]:
#mins in 2050 and 2100
cumu_carbon_2050 = sim_cumu_carbon.loc[2050].filter(like='Annual_Emit_total_modmats')/1e12
cumu_carbon_2100 = sim_cumu_carbon.loc[2100].filter(like='Annual_Emit_total_modmats')/1e12
cumu_carbon_rankings_crittime = pd.concat([cumu_carbon_2050,cumu_carbon_2100], axis=1)
cumu_carbon_rankings_crittime.index = scennames_labels_flat
cumu_carbon_rankings_crittime_bmt = cumu_carbon_rankings_crittime/1000
round(cumu_carbon_rankings_crittime_bmt,1)
Out[52]:
2050 2100
PV_ICE 14.1 29.6
PERC 12.8 22.1
SHJ 12.2 21.0
TOPCon 12.4 21.5
Low Quality 15.2 32.2
Long-Lived 13.4 22.7
High Eff 10.9 30.5
Circular 17.6 28.0
High Eff + Long-life 11.2 23.0
Long-Life + Recycling 13.4 22.8
Recycled-Si + Long-life 14.3 23.5
Circular + Long-life 18.3 27.0
Circular + High Eff 15.1 23.5
In [53]:
cumu_carbon_rankings_crittime_plot = cumu_carbon_rankings_crittime.copy()
cumu_carbon_rankings_crittime_plot['diff'] = cumu_carbon_rankings_crittime[2100]-cumu_carbon_rankings_crittime[2050]
In [54]:
fig_cumulativeemit, (ax0,ax2,ax3) = plt.subplots(1,3,figsize=(15,8), sharey=True, 
                                      gridspec_kw={'wspace': 0, 'width_ratios': [1.5,1,1.5]})
#BAU
ax0.bar(cumu_carbon_rankings_crittime_plot.index[0:5], cumu_carbon_rankings_crittime_plot[2050].iloc[0:5],
        tick_label=scennames_labels[0:5], color=colorpalette[0:5], alpha = 0.7, edgecolor='white')
ax0.bar(cumu_carbon_rankings_crittime_plot.index[0:5], cumu_carbon_rankings_crittime_plot['diff'].iloc[0:5],
        bottom=cumu_carbon_rankings_crittime_plot[2050].iloc[0:5],
        tick_label=scennames_labels[0:5], color=colorpalette[0:5])
ax0.set_ylim(0,maxy+1000)
ax0.set_ylabel('Cumulative Carbon Emissions\n[million metric tonnes $CO_{2eq}$]', fontsize=20)
ax0.set_title('Baseline', fontsize=14)
ax0.set_xticklabels(labels=scennames_labels[0:5], rotation=45)
ax0.grid(axis='y', color='0.6', ls='--') 
ax0.set_axisbelow(True)

#Extreme
ax2.bar(cumu_carbon_rankings_crittime_plot.index[5:8], cumu_carbon_rankings_crittime_plot[2050].iloc[5:8],
        tick_label=scennames_labels[5:8], color=colorpalette[5:8], alpha = 0.7, edgecolor='white')
ax2.bar(cumu_carbon_rankings_crittime_plot.index[5:8], cumu_carbon_rankings_crittime_plot['diff'].iloc[5:8],
        bottom=cumu_carbon_rankings_crittime_plot[2050].iloc[5:8],
        tick_label=scennames_labels[5:8], color=colorpalette[5:8])
ax2.set_title('Extreme', fontsize=14)
ax2.set_xticklabels(labels=scennames_labels[5:8], rotation=45)
ax2.grid(axis='y', color='0.6', ls='--') 
ax2.set_axisbelow(True)

#Ambitious
ax3.bar(cumu_carbon_rankings_crittime_plot.index[8:], cumu_carbon_rankings_crittime_plot[2050].iloc[8:],
        tick_label=scennames_labels[8:], color=colorpalette[8:], hatch='x', edgecolor='white', alpha=0.7)
ax3.bar(cumu_carbon_rankings_crittime_plot.index[8:], cumu_carbon_rankings_crittime_plot['diff'].iloc[8:],
        bottom=cumu_carbon_rankings_crittime_plot[2050].iloc[8:],
        tick_label=scennames_labels[8:], color=colorpalette[8:], hatch='x', edgecolor='white')
ax3.set_title('Ambitious', fontsize=14)
ax3.set_xticklabels(labels=scennames_labels[8:], rotation=45)
ax3.grid(axis='y', color='0.6', ls='--') 
ax3.set_axisbelow(True)

#overall fig

fig_cumulativeemit.suptitle('Cumulative Emissions in 2050, 2100', fontsize=24)
plt.show()

#fig_eBalance.savefig('energyresults-energyBalance.png', dpi=300, bbox_inches='tight')

¶

Emissions by electricity vs fuels vs process¶

Process emission summing¶

This only happens on the material files

In [55]:
process_emissions = pd.DataFrame()
for scen in scenarios:
    scen_p = sim_carbon_dfs.filter(like=scen).filter(like='_p_')
    scen_p_sum = scen_p.sum(axis=1)
    process_emissions = pd.concat([process_emissions,scen_p_sum], axis=1)

process_emissions.columns = scennames_labels_flat
process_emissions.index = pd.RangeIndex(start=2000,stop=2101,step=1)
process_emissions_cumu = process_emissions.cumsum()
In [56]:
#process_emissions_cumu

Fuel Emissions¶

This is capturing steam and heating fuel, also only on material level

In [57]:
fuel_emissions = pd.DataFrame()
for scen in scenarios:
    scen_f = sim_carbon_dfs.filter(like=scen).filter(like='_fuel_')
    scen_f_sum = scen_f.sum(axis=1)
    fuel_emissions = pd.concat([fuel_emissions,scen_f_sum], axis=1)
    
fuel_emissions.columns = scennames_labels_flat
fuel_emissions.index = pd.RangeIndex(start=2000,stop=2101,step=1)
fuel_emissions_cumu = fuel_emissions.cumsum()
In [58]:
#fuel_emissions_cumu

Electricity Emissions¶

both module and material level elec.

In [59]:
elec_emissions = pd.DataFrame()
for scen in scenarios:
    scen_mod_elec = sim_carbon_dfs.filter(like=scen).filter(like='Global_mod_MFG') #module elec lifecycle emits
    
    #material elec emits
    mat_eleckey = ['Global_vmfg_elec','landfill_elec','ReMFG_clean_elec','Recycled_LQ_elec','Recycled_HQ_elec']
    mat_elecs_search = '|'.join(mat_eleckey)
    scen_mat_elecs = sim_carbon_dfs.loc[:,sim_carbon_dfs.columns.str.contains(mat_elecs_search)].filter(like=scen)
    scen_mat_elecs_sum = scen_mat_elecs.sum(axis=1)
    
    #sum them together by scen
    scen_elec_modmat_annual_sum = scen_mat_elecs_sum+scen_mod_elec.iloc[:,0]
    elec_emissions = pd.concat([elec_emissions,scen_elec_modmat_annual_sum], axis=1)
    
elec_emissions.columns=scennames_labels_flat
elec_emissions.index = pd.RangeIndex(start=2000,stop=2101,step=1)
elec_emissions_cumu = elec_emissions.cumsum()
In [60]:
#graphing by emission source
efp_emit_total = elec_emissions+fuel_emissions+process_emissions
efp_emit_total_cumu = elec_emissions_cumu+fuel_emissions_cumu+process_emissions_cumu
In [61]:
#graphing by emission source, annual
#efp_emit_total = elec_emissions+fuel_emissions+process_emissions

for scen in scennames_labels_flat:
    
    plt.plot([],[],color='black', label='process')
    plt.plot([],[],color='darkred', label='fuel')
    plt.plot([],[],color='blue', label='electricity')

    plt.stackplot(elec_emissions.index,
                  process_emissions[scen]/1e12, 
                  fuel_emissions[scen]/1e12,
                  elec_emissions[scen]/1e12, 
                  colors = ['black','darkred','blue'])
    plt.title(scen+':\nGHG Emissions Annually by Source')
    plt.ylabel('GHG Emissions Annually from Lifecycle Source\n[million metric tonnes $CO_{2eq}$]')
    plt.xlim(2000,2100)
    plt.ylim(0,)

    handles, labels = plt.gca().get_legend_handles_labels()
#specify order of items in legend
#order = [1,2,0]
#add legend to plot
#plt.legend([handles[idx] for idx in order],[labels[idx] for idx in order])
    plt.legend(handles[::-1], labels[::-1], bbox_to_anchor=(1.4,1))
    #plt.grid(axis='both', which='both', color='0.9', ls='--', zorder=0)
    #plt.set_axisbelow(True)
#plt.legend()
    plt.show()
In [62]:
#graphing by emission source, cumulative

for scen in scennames_labels_flat:
    
    plt.plot([],[],color='black', label='process')
    plt.plot([],[],color='darkred', label='fuel')
    plt.plot([],[],color='blue', label='electricity')

    plt.stackplot(elec_emissions_cumu.index,
                  process_emissions_cumu[scen]/1e12, 
                  fuel_emissions_cumu[scen]/1e12,
                  elec_emissions_cumu[scen]/1e12, 
                  colors = ['black','darkred','blue'])
    plt.title(scen+':\nGHG Emissions Cumulative by Source')
    plt.ylabel('GHG Emissions Cumulatively from Lifecycle Source\n[million metric tonnes $CO_{2eq}$]')
    plt.xlim(2000,2100)
    plt.ylim(0,maxy+1000)

    handles, labels = plt.gca().get_legend_handles_labels()
#specify order of items in legend
#order = [1,2,0]
#add legend to plot
#plt.legend([handles[idx] for idx in order],[labels[idx] for idx in order])
    plt.legend(handles[::-1], labels[::-1], bbox_to_anchor=(1.3,1))
    plt.grid(axis='both', which='both', color='0.9', ls='--')
#plt.legend()
    plt.show()
In [63]:
#bar chart 2050 and 2100 by scenario by emission source
emit_efp_2100_forbar = pd.concat([elec_emissions_cumu.loc[2100],fuel_emissions_cumu.loc[2100],process_emissions_cumu.loc[2100]],
                                 axis=1,keys=['electricity','fuel','process'])
In [64]:
emit_efp_2100_mmt = emit_efp_2100_forbar/1e12
In [65]:
fig_cumulativeemit, (ax0,ax2,ax3) = plt.subplots(1,3,figsize=(15,8), sharey=True, 
                                      gridspec_kw={'wspace': 0, 'width_ratios': [1.5,1,1.5]})
#BAU
ax0.bar(emit_efp_2100_mmt.index[0:5], emit_efp_2100_mmt['process'].iloc[0:5],label='process',
        tick_label=scennames_labels[0:5], color='black')

ax0.bar(emit_efp_2100_mmt.index[0:5], emit_efp_2100_mmt['fuel'].iloc[0:5],label='fuel',
        bottom=emit_efp_2100_mmt['process'].iloc[0:5], 
        tick_label=scennames_labels[0:5], color='darkred')

ax0.bar(emit_efp_2100_mmt.index[0:5], emit_efp_2100_mmt['electricity'].iloc[0:5],label='electricity',
        bottom=emit_efp_2100_mmt['process'].iloc[0:5]+emit_efp_2100_mmt['fuel'].iloc[0:5],
        tick_label=scennames_labels[0:5], color='blue')

ax0.set_ylim(0,maxy+1000)
ax0.set_ylabel('Cumulative Carbon Emissions\n[million metric tonnes $CO_{2eq}$]', fontsize=20)
ax0.set_title('Baseline', fontsize=14)
ax0.set_xticklabels(labels=scennames_labels[0:5], rotation=45)
ax0.grid(axis='y', color='0.6', ls='--') 
ax0.set_axisbelow(True)

#Extreme
ax2.bar(emit_efp_2100_mmt.index[5:8], emit_efp_2100_mmt['process'].iloc[5:8],
        tick_label=scennames_labels[5:8], color='black')

ax2.bar(emit_efp_2100_mmt.index[5:8], emit_efp_2100_mmt['fuel'].iloc[5:8],
        bottom=emit_efp_2100_mmt['process'].iloc[5:8], 
        tick_label=scennames_labels[5:8], color='darkred')

ax2.bar(emit_efp_2100_mmt.index[5:8], emit_efp_2100_mmt['electricity'].iloc[5:8],
        bottom=emit_efp_2100_mmt['process'].iloc[5:8]+emit_efp_2100_mmt['fuel'].iloc[5:8],
        tick_label=scennames_labels[5:8], color='blue')

ax2.set_title('Extreme', fontsize=14)
ax2.set_xticklabels(labels=scennames_labels[5:8], rotation=45)
ax2.grid(axis='y', color='0.6', ls='--') 
ax2.set_axisbelow(True)

#Ambitious
ax3.bar(emit_efp_2100_mmt.index[8:], emit_efp_2100_mmt['process'].iloc[8:],
        tick_label=scennames_labels[8:], color='black')

ax3.bar(emit_efp_2100_mmt.index[8:], emit_efp_2100_mmt['fuel'].iloc[8:],
        bottom=emit_efp_2100_mmt['process'].iloc[8:], 
        tick_label=scennames_labels[8:], color='darkred')

ax3.bar(emit_efp_2100_mmt.index[8:], emit_efp_2100_mmt['electricity'].iloc[8:],
        bottom=emit_efp_2100_mmt['process'].iloc[8:]+emit_efp_2100_mmt['fuel'].iloc[8:],
        tick_label=scennames_labels[8:], color='blue')

ax3.set_title('Ambitious', fontsize=14)
ax3.set_xticklabels(labels=scennames_labels[8:], rotation=45)
ax3.grid(axis='y', color='0.6', ls='--') 
ax3.set_axisbelow(True)

#overall fig
fig_cumulativeemit.suptitle('Cumulative Emisisons in 2100 by emission source', fontsize=24)
handles, labels = plt.gca().get_legend_handles_labels()
plt.legend(handles[::-1], labels[::-1], bbox_to_anchor=(1.42,1))
plt.show()

#fig_eBalance.savefig('energyresults-energyBalance.png', dpi=300, bbox_inches='tight')
In [ ]:

vMFG versus Circular Processes¶

3 categories for this:

  • vmfg of materials
  • CE of materials and materials
  • install/decomission requirements and I think module mfging falls into the last category
In [66]:
CEkey = ['ReMFG','Recycle_Crush','Recycled_LQ','Recycled_HQ','Resell','Repair','LQ','HQ']
CEkey_search = '|'.join(CEkey)

LinearKey = ['vMFG', 'vmfg', 'landfill']
LinearKey_search = '|'.join(LinearKey)

otherkey = ['mod_MFG','OandM','Install','Demount', 'Store'] #THIS ONE IS MISSING COLUMNS!!!
otherkey_search = '|'.join(otherkey)

scen_carbon_CE.columns

scen_carbon_linear.columns

scen_carbon_other.columns

allcolumns = list(scen_annual_carbon.columns) selectedcolumns = list(scen_carbon_other.columns)+list(scen_carbon_linear.columns)+list(scen_carbon_CE.columns) selectedcolumns

set(allcolumns).difference(selectedcolumns)

In [67]:
#subset by pathways
sim_carbon_CE = pd.DataFrame()
sim_carbon_linear = pd.DataFrame()
sim_carbon_necessary = pd.DataFrame()

for scen in scenarios:
    scen_annual_carbon = sim_annual_carbon.filter(like=scen)
    
    scen_carbon_CE = scen_annual_carbon.loc[:,scen_annual_carbon.columns.str.contains(CEkey_search)]
    scen_carbon_CE_sum = scen_carbon_CE.sum(axis=1)
    sim_carbon_CE = pd.concat([sim_carbon_CE, scen_carbon_CE_sum], axis=1)
    
    scen_carbon_linear = scen_annual_carbon.loc[:,scen_annual_carbon.columns.str.contains(LinearKey_search)]
    scen_carbon_linear_sum = scen_carbon_linear.sum(axis=1)
    sim_carbon_linear = pd.concat([sim_carbon_linear,scen_carbon_linear_sum ], axis=1)
    
    scen_carbon_other = scen_annual_carbon.loc[:,scen_annual_carbon.columns.str.contains(otherkey_search)]
    scen_carbon_other_sum = scen_carbon_other.sum(axis=1)
    sim_carbon_necessary = pd.concat([sim_carbon_necessary, scen_carbon_other_sum], axis=1)
    
sim_carbon_CE.columns = scennames_labels_flat
sim_carbon_linear.columns = scennames_labels_flat
sim_carbon_necessary.columns = scennames_labels_flat

#.index = pd.RangeIndex(start=2000,stop=2101,step=1)
In [68]:
sim_carbon_CE_cumu = sim_carbon_CE.cumsum()
sim_carbon_linear_cumu = sim_carbon_linear.cumsum()
sim_carbon_necessary_cumu = sim_carbon_necessary.cumsum()
In [69]:
emit_pathway = pd.concat([sim_carbon_CE_cumu.loc[2100],sim_carbon_linear_cumu.loc[2100],sim_carbon_necessary_cumu.loc[2100]],
                                 axis=1,keys=['Circular','Linear/Virgin','Necessary'])
emit_pathway_mmt = emit_pathway/1e12
In [70]:
sim_carbon_CE_cumu
Out[70]:
PV_ICE PERC SHJ TOPCon Low Quality Long-Lived High Eff Circular High Eff + Long-life Long-Life + Recycling Recycled-Si + Long-life Circular + Long-life Circular + High Eff
2000 1.243800e+08 1.081060e+08 1.038402e+08 1.059533e+08 1.243800e+08 1.159678e+08 1.034222e+08 1.243800e+08 1.034222e+08 1.159678e+08 1.243800e+08 1.243800e+08 1.243800e+08
2001 1.671031e+08 1.557593e+08 1.527859e+08 1.542588e+08 1.671031e+08 1.612394e+08 1.524945e+08 1.671031e+08 1.524945e+08 1.612394e+08 1.671031e+08 1.671031e+08 1.671031e+08
2002 2.168491e+08 2.022509e+08 1.984243e+08 2.003199e+08 2.168491e+08 2.093032e+08 1.980494e+08 2.168491e+08 1.980494e+08 2.093032e+08 2.168491e+08 2.168491e+08 2.168491e+08
2003 2.898076e+08 2.704357e+08 2.653579e+08 2.678733e+08 2.898039e+08 2.797941e+08 2.648604e+08 2.898076e+08 2.648604e+08 2.797941e+08 2.898076e+08 2.898076e+08 2.898076e+08
2004 4.327494e+08 4.040232e+08 3.964934e+08 4.002235e+08 4.327160e+08 4.179006e+08 3.957556e+08 4.327494e+08 3.957556e+08 4.179006e+08 4.327494e+08 4.327494e+08 4.327494e+08
... ... ... ... ... ... ... ... ... ... ... ... ... ...
2096 9.539486e+12 1.137631e+15 1.082464e+15 1.105431e+15 2.100357e+15 4.351846e+10 4.124516e+10 1.457239e+15 4.124516e+10 1.552144e+14 8.824872e+14 8.160076e+14 9.022651e+14
2097 9.675062e+12 1.157989e+15 1.101828e+15 1.125207e+15 2.155752e+15 4.351846e+10 4.124516e+10 1.477129e+15 4.124516e+10 1.645741e+14 9.081595e+14 8.216632e+14 9.161259e+14
2098 9.822367e+12 1.166562e+15 1.109981e+15 1.133535e+15 2.211400e+15 4.351846e+10 4.124516e+10 1.497469e+15 4.124516e+10 1.741338e+14 9.347823e+14 8.276286e+14 9.303606e+14
2099 9.981563e+12 1.175323e+15 1.118315e+15 1.142046e+15 2.266708e+15 4.351846e+10 4.124516e+10 1.518299e+15 4.124516e+10 1.838479e+14 9.632659e+14 8.340084e+14 9.450031e+14
2100 1.015567e+13 1.184348e+15 1.126899e+15 1.150813e+15 2.322056e+15 4.351846e+10 4.124516e+10 1.539641e+15 4.124516e+10 1.936557e+14 9.933969e+14 8.551872e+14 9.600724e+14

101 rows × 13 columns

In [71]:
#emit_pathway.sum(axis=1) #check that matches, we're good
emit_pathway_mmt
Out[71]:
Circular Linear/Virgin Necessary
PV_ICE 10.155668 29545.307734 86.608227
PERC 1184.347807 20841.381042 79.515163
SHJ 1126.898709 19841.951078 75.719958
TOPCon 1150.813312 20260.393902 77.296139
Low Quality 2322.055728 29791.554269 101.376302
Long-Lived 0.043518 22665.530556 81.124405
High Eff 0.041245 30470.200356 64.810270
Circular 1539.641044 26384.678193 123.840121
High Eff + Long-life 0.041245 22888.315668 68.646302
Long-Life + Recycling 193.655661 22563.190574 81.610618
Recycled-Si + Long-life 993.396948 22176.873348 302.010424
Circular + Long-life 855.187166 26007.628613 117.086784
Circular + High Eff 960.072447 22427.714767 105.292637
In [72]:
fig_emitByPathway, (ax0,ax2,ax3) = plt.subplots(1,3,figsize=(15,8), sharey=True, 
                                      gridspec_kw={'wspace': 0, 'width_ratios': [1.5,1,1.5]})
#BAU
ax0.bar(emit_pathway_mmt.index[0:5], emit_pathway_mmt['Necessary'].iloc[0:5],label='Necessary',
        tick_label=scennames_labels[0:5], color='darkgray', edgecolor='black')

ax0.bar(emit_pathway_mmt.index[0:5], emit_pathway_mmt['Linear/Virgin'].iloc[0:5],label='Linear/Virgin',
        bottom=emit_pathway_mmt['Necessary'].iloc[0:5], 
        tick_label=scennames_labels[0:5], color='darkorange')

ax0.bar(emit_pathway_mmt.index[0:5], emit_pathway_mmt['Circular'].iloc[0:5],label='Circular',
        bottom=emit_pathway_mmt['Necessary'].iloc[0:5]+emit_pathway_mmt['Linear/Virgin'].iloc[0:5],
        tick_label=scennames_labels[0:5], color='green')

ax0.set_ylim(0,maxy+1000)
ax0.set_ylabel('Cumulative Carbon Emissions\n[million metric tonnes $CO_{2eq}$]', fontsize=20)
ax0.set_title('Baseline', fontsize=14)
ax0.set_xticklabels(labels=scennames_labels[0:5], rotation=45)
ax0.grid(axis='y', color='0.6', ls='--') 
ax0.set_axisbelow(True)

#Extreme
ax2.bar(emit_pathway_mmt.index[5:8], emit_pathway_mmt['Necessary'].iloc[5:8],label='Necessary',
        tick_label=scennames_labels[5:8], color='darkgray', edgecolor='black')

ax2.bar(emit_pathway_mmt.index[5:8], emit_pathway_mmt['Linear/Virgin'].iloc[5:8],label='Linear/Virgin',
        bottom=emit_pathway_mmt['Necessary'].iloc[5:8], 
        tick_label=scennames_labels[5:8], color='darkorange')

ax2.bar(emit_pathway_mmt.index[5:8], emit_pathway_mmt['Circular'].iloc[5:8],label='Circular',
        bottom=emit_pathway_mmt['Necessary'].iloc[5:8]+emit_pathway_mmt['Linear/Virgin'].iloc[5:8],
        tick_label=scennames_labels[5:8], color='green')

ax2.set_title('Extreme', fontsize=14)
ax2.set_xticklabels(labels=scennames_labels[5:8], rotation=45)
ax2.grid(axis='y', color='0.6', ls='--') 
ax2.set_axisbelow(True)

#Ambitious
ax3.bar(emit_pathway_mmt.index[8:], emit_pathway_mmt['Necessary'].iloc[8:],label='Necessary',
        tick_label=scennames_labels[8:], color='darkgray', edgecolor='black')

ax3.bar(emit_pathway_mmt.index[8:], emit_pathway_mmt['Linear/Virgin'].iloc[8:],label='Linear/Virgin',
        bottom=emit_pathway_mmt['Necessary'].iloc[8:], 
        tick_label=scennames_labels[8:], color='darkorange')

ax3.bar(emit_pathway_mmt.index[8:], emit_pathway_mmt['Circular'].iloc[8:],label='Circular',
        bottom=emit_pathway_mmt['Necessary'].iloc[8:]+emit_pathway_mmt['Linear/Virgin'].iloc[8:],
        tick_label=scennames_labels[8:], color='green')

ax3.set_title('Ambitious', fontsize=14)
ax3.set_xticklabels(labels=scennames_labels[8:], rotation=45)
ax3.grid(axis='y', color='0.6', ls='--') 
ax3.set_axisbelow(True)

#overall fig
fig_emitByPathway.suptitle('Cumulative Emissions in 2100 by CE Category', fontsize=24)
handles, labels = plt.gca().get_legend_handles_labels()
plt.legend(handles[::-1], labels[::-1], bbox_to_anchor=(1.5,1))
plt.show()

#fig_eBalance.savefig('energyresults-energyBalance.png', dpi=300, bbox_inches='tight')
In [ ]:
In [ ]:

Emissions per Capacity¶

Trying to quantify the emissions entailed in achieving energy transition target capacities. Our current calculations don't allow a good method of CO2/kWh, but we do know how much it now takes to achieve the first 75 TW then the the next 11 TW will entail a different amount of carbon. This may be a valuable comparison

In [73]:
cumu_carbon_rankings_crittime#.loc[scen,2050]
Out[73]:
2050 2100
PV_ICE 14059.020878 29642.071628
PERC 12800.290860 22105.244013
SHJ 12193.770105 21044.569746
TOPCon 12448.897638 21488.503353
Low Quality 15169.837923 32214.986299
Long-Lived 13425.139177 22746.698479
High Eff 10927.903642 30535.051871
Circular 17620.780689 28048.159358
High Eff + Long-life 11214.153901 22957.003214
Long-Life + Recycling 13441.057091 22838.456854
Recycled-Si + Long-life 14299.423757 23472.280720
Circular + Long-life 18319.502170 26979.902562
Circular + High Eff 15083.986429 23493.079852
In [74]:
cumu_carbon_rankings_crittime.index = scenarios #relabel the index for the calc
scen_carbonPERcapacity = pd.DataFrame(index=scenarios)
for scen in scenarios:
    #2050
    scen_effectiveCap_TW = sim1.scenario[scen].dataOut_m.loc[50, 'Effective_Capacity_[W]']/1e12
    scen_carbon_mmt = cumu_carbon_rankings_crittime.loc[scen,2050]
    scen_carbonPERcapacity.loc[scen, 'EffectiveCap_TW_2050'] = scen_effectiveCap_TW
    scen_carbonPERcapacity.loc[scen, 'Carbon_mmt_2050'] = scen_carbon_mmt
    scen_carbonPERcapacity.loc[scen, 'CO2pTW_2050'] = scen_carbon_mmt/scen_effectiveCap_TW
    #2100
    scen_effectiveCap_TW_2100 = sim1.scenario[scen].dataOut_m.loc[100, 'Effective_Capacity_[W]']/1e12
    scen_carbon_mmt_2100 = cumu_carbon_rankings_crittime.loc[scen,2100]
    scen_carbonPERcapacity.loc[scen, 'EffectiveCap_TW_2100'] = scen_effectiveCap_TW_2100
    scen_carbonPERcapacity.loc[scen, 'Carbon_mmt_2100'] = scen_carbon_mmt_2100
    scen_carbonPERcapacity.loc[scen, 'CO2pTW_2100'] = scen_carbon_mmt_2100/scen_effectiveCap_TW_2100
    #marginal increase between 2050 and 2100
In [75]:
round(scen_carbonPERcapacity,0)
Out[75]:
EffectiveCap_TW_2050 Carbon_mmt_2050 CO2pTW_2050 EffectiveCap_TW_2100 Carbon_mmt_2100 CO2pTW_2100
PV_ICE 75.0 14059.0 187.0 86.0 29642.0 345.0
r_PERC 75.0 12800.0 171.0 86.0 22105.0 257.0
r_SHJ 75.0 12194.0 163.0 86.0 21045.0 245.0
r_TOPCon 75.0 12449.0 166.0 86.0 21489.0 250.0
r_IRENA 75.0 15170.0 202.0 86.0 32215.0 375.0
ex_Life 75.0 13425.0 179.0 86.0 22747.0 265.0
ex_High_eff 75.0 10928.0 146.0 86.0 30535.0 355.0
ex_Circular 75.0 17621.0 235.0 86.0 28048.0 326.0
h_EffLife 75.0 11214.0 150.0 86.0 22957.0 267.0
h_50PERC 75.0 13441.0 179.0 86.0 22838.0 266.0
h_RecycledPERC 75.0 14299.0 191.0 86.0 23472.0 273.0
h_Perovskite_life 75.0 18320.0 244.0 86.0 26980.0 314.0
h_Perovskite_Eff 75.0 15084.0 201.0 86.0 23493.0 273.0
In [ ]:
In [ ]:
In [ ]: